Final answer:
To count NaN values in a Pandas DataFrame, use the isna() method combined with the sum() method. Apply sum() twice to get the total count of NaNs, or once along the desired axis to get counts by column or row. This technique is crucial for data preprocessing in Pandas.
Step-by-step explanation:
To count NaN values in a Pandas DataFrame, you can use the isna() method, which returns a boolean DataFrame indicating if the values are NaN or not. Then, you can use the sum() method to count the True values, which correspond to NaN values. Performing the sum operation twice, once for the rows and once more for the result, gives you the total number of NaN values in the DataFrame.
If you want to count NaN values column-wise or row-wise, you can apply the sum method accordingly. Here is an example:
- For counting NaN values in the entire DataFrame: total_nans = df.isna().sum().sum()
- For counting NaN values for each column: nans_by_column = df.isna().sum()
- For counting NaN values for each row: nans_by_row = df.isna().sum(axis=1)
Remember to import Pandas before you begin your calculations. The above methods can be used to efficiently count NaN values and are very useful for data cleaning and preprocessing steps in data analysis.